Electrostimulation to reduce synaptic scaling driven progression of Alzheimers (Rowan et al. 2014)

 Download zip file   Auto-launch 
Help downloading and running models
Accession:154096
"... As cells die and synapses lose their drive, remaining cells suffer an initial decrease in activity. Neuronal homeostatic synaptic scaling then provides a feedback mechanism to restore activity. ... The scaling mechanism increases the firing rates of remaining cells in the network to compensate for decreases in network activity. However, this effect can itself become a pathology, ... Here, we present a mechanistic explanation of how directed brain stimulation might be expected to slow AD progression based on computational simulations in a 470-neuron biomimetic model of a neocortical column. ... "
Reference:
1 . Rowan MS, Neymotin SA, Lytton WW (2014) Electrostimulation to reduce synaptic scaling driven progression of Alzheimer's disease. Front Comput Neurosci 8:39 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism: Neocortex;
Cell Type(s): Neocortex V1 pyramidal corticothalamic L6 cell; Neocortex V1 pyramidal intratelencephalic L2-5 cell; Neocortex V1 interneuron basket PV cell; Neocortex fast spiking (FS) interneuron; Neocortex spiny stellate cell; Neocortex spiking regular (RS) neuron; Neocortex spiking low threshold (LTS) neuron;
Channel(s):
Gap Junctions:
Receptor(s): GabaA; AMPA; NMDA;
Gene(s):
Transmitter(s): Gaba; Glutamate;
Simulation Environment: NEURON; Python;
Model Concept(s): Long-term Synaptic Plasticity; Aging/Alzheimer`s; Deep brain stimulation; Homeostasis;
Implementer(s): Lytton, William [billl at neurosim.downstate.edu]; Neymotin, Sam [samn at neurosim.downstate.edu]; Rowan, Mark [m.s.rowan at cs.bham.ac.uk];
Search NeuronDB for information about:  Neocortex V1 pyramidal corticothalamic L6 cell; Neocortex V1 pyramidal intratelencephalic L2-5 cell; Neocortex V1 interneuron basket PV cell; GabaA; AMPA; NMDA; Gaba; Glutamate;
/
RowanEtAl2014
batchscripts
mod
README
alz.hoc
alzinfo.m
autotune.hoc *
basestdp.hoc *
batch.hoc *
batch2.hoc *
batchcommon
checkirreg.hoc *
clusterrun.sh
col.dot *
col.hoc *
comppowspec.hoc *
condisconcellfig.hoc *
condisconpowfig.hoc *
declist.hoc *
decmat.hoc *
decnqs.hoc *
decvec.hoc *
default.hoc *
drline.hoc *
e2hubsdisconpow.hoc *
e2incconpow.hoc *
filtutils.hoc *
flexinput.hoc
geom.hoc *
graphplug.hoc *
grvec.hoc *
infot.hoc *
init.hoc *
labels.hoc *
load.hoc *
local.hoc *
makepopspikenq.hoc *
matfftpowplug.hoc *
matpmtmplug.hoc *
matpmtmsubpopplug.hoc *
matspecplug.hoc *
mosinit.hoc
network.hoc *
nload.hoc *
nqpplug.hoc *
nqs.hoc *
nqsnet.hoc *
nrnoc.hoc *
params.hoc
plot.py
plotavg.py
plotbatch.sh
plotbatchcluster.sh
plotdeletions.py
plotntes.py
powchgtest.hoc *
pyhoc.py
python.hoc *
pywrap.hoc *
ratlfp.dat *
redE2.hoc *
run.hoc
runsim.sh
setup.hoc *
shufmua.hoc *
sim.hoc
simctrl.hoc *
spkts.hoc *
stats.hoc *
syncode.hoc *
vsampenplug.hoc *
writedata.hoc
xgetargs.hoc *
                            
// $Id: decnqs.hoc,v 1.38 2011/03/01 19:06:15 billl Exp $

print "Loading decnqs.hoc..."

{load_file("nqs.hoc")}
objref nq[10],pq[10]

//** prl2nqs(NQS[,min,max,nointerp]) -- transfer printlist to NQS
proc rename () {}
// eg proc rename () { sprint($s1,"P%d",objnum($s1)) }
obfunc prl2nqs () { local tstep localobj st,oq
  st=new String2()
  oq=new NQS()
  if (numarg()>=1) min=$1 else min=0
  if (numarg()>=2) max=$2 else max=printlist.count-1
  if (numarg()>=3) interp=$3 else interp=0 // no interp when looking at spk times
  if (interp) oq.resize(max-min+2)
  if (interp) {
    tstep=0.1 // 0.1 ms step size for interpolation
    oq.s[0].s="time"
    oq.v[0].indgen(0,printlist.object(0).tvec.max,tstep)
    for ii=min,max {
      XO=printlist.object(ii)
      oq.s[ii+1-min].s = XO.var
      rename(oq.s[ii+1-min].s)
      oq.v[ii+1-min].resize(oq.v.size)
      oq.v[ii+1-min].interpolate(oq.v[0],XO.tvec,XO.vec)
    }
  } else {
    for ii=min,max {
      XO=printlist.object(ii)
      st.s=XO.name
      sprint(st.t,"%s-time",XO.name)
      rename(st.s) rename(st.t)
      oq.resize(st.s,st.t)
      oq.setcols(XO.vec,XO.tvec)
    }
  }
  return oq
}

//** pvp2nqs(NQS) -- transfer grvec data file to NQS
obfunc pvp2nqs () { local min,max,interp,gvnum,ii,jj,n localobj oq,po,st,xo
  interp=min=max=gvnum=0
  if (argtype(1)==2) { po=gvnew($s1) gvnum=panobjl.count()-1 }
  if (argtype(1)==0) gvnum=$1
  if (numarg()>=2) min=$2
  if (numarg()>=3) max=$3
  if (numarg()>=4) interp=$4 // no interp when looking at spk times
  if (po==nil) po=panobjl.o(gvnum)
  oq=new NQS() st=new String2()
  if (gvnum>0) {
    if (max==0) max=po.llist.count()-1
    for ii=min,max {
      xo=po.llist.object(ii)
      po.tmpfile.seek(xo.loc)
      if (xo.num==-2) {
        sprint(st.s,"%s-time",xo.name)
        jj=oq.resize(st.s)-1
        oq.v[jj].vread(po.tmpfile)
      }
      jj=oq.resize(xo.name)-1
      oq.v[jj].vread(po.tmpfile)
    }
  } else { // from printlist
    if (max==0) max=printlist.count()-1
    for ii=min,max {
      xo=printlist.o(ii)
      jj=oq.resize(xo.name)-1
      oq.v[jj].copy(xo.vec)
      if (xo.pstep==0) {
        sprint(st.s,"%s-time",xo.name)
        jj=oq.resize(st.s)-1
        oq.v[jj].copy(xo.tvec)
      }
    }
  }
  return oq
}

//** veclist2nqs(nqs[,STR1,STR2,...])
proc veclist2nqs () { local flag,i
  if (numarg()==0) {printf("veclist2nqs(nqs[,STR1,STR2,...])\n") return}
  $o1.resize(veclist.count)
  if (numarg()==1+$o1.m) flag=1 else flag=0
  for ltr(XO,veclist) {
    $o1.v[i1].copy(XO)
    if (flag) {i=i1+2 $o1.s[i1].s=$si} else {sprint(tstr,"v%d",i1) $o1.s[i1].s=tstr}
  }
  $o1.cpout()
}

// fudup(vec[,nq,#CUTS,LOGCUT,MIN]) -- use updown() to find spikes
// LOC(0) PEAK(1) WIDTH(2) BASE(3) HEIGHT(4) START(5) SLICES(6) SHARP(7) INDEX(8) FILE(9) NESTED(10)
// other options
pos_fudup=1 // set to 1 to move whole curve up above 0
maxp_fudup=0.95 // draw top sample at 95% of max 
minp_fudup=0.05 // draw bottom sample at 5% of max
over_fudup=1    // turn over and try again if nothing found
allover_fudup=0 // turn over and add these locs (not debugged)
verbose_fudup=0 // give messages, can also turn on DEBUG_VECST for messages from updown()
obfunc fudup () { local a,i,ii,npts,logflag,min,x,sz localobj bq,cq,v1,v2,v3,bb,tl,v5,eq
  if (verbose_fudup) printf("MAXTIME appears to be %g (dt=%g)\n",$o1.size*dt,dt)
  logflag=0  npts=10 // n sample locations by default
  bq=new NQS("LOC","PEAK","WIDTH","BASE","HEIGHT","START","SLICES","SHARP","INDEX","FILE","NESTED") 
  i=2
  if (argtype(i)==1) {i+=1 if ($o2==nil) {cq=new NQS() $o2=cq} else cq=$o2} else cq=new NQS()
  if (cq.m!=11) { cq.resize(0) 
  cq.resize("LOC","PEAK","WIDTH","BASE","HEIGHT","START","SLICES","SHARP","INDEX","FILE","NESTED")}
  if (argtype(i)==0){ npts=$i i+=1
    if (argtype(i)==0){ logflag=$i i+=1
      if (argtype(i)==1) { v5=$oi i+=1
        if (npts!=v5.size) printf("Correcting npts from %d to %d\n",npts,npts=v5.size)
        if (v5.ismono(1)) v5.reverse
        if (! v5.ismono(-1)) {printf("fudup: final arg (%s) must be monotonic\n",v5) return}
      }
    }
  }
  bq.listvecs(bb)
  bq.pad(5000)
  eq=new NQS(-2,npts) a=allocvecs(v1,v2,v3)
  tl=eq.vl
  eq.clear(2e4) vrsz(2e4,v1,v2,v3)
  v1.copy($o1)
  if (pos_fudup) {
    min=v1.min
    v1.sub(min) // make it uniformly positive
  } else min=0
  if (numarg()>4) v2.copy(v5) else {
    v2.indgen(2,2+npts-1,1)   // sampling at npts points, start at 2 to avoid log(1)=0
    if (logflag) v2.log() // log sampling
    v2.scale(-maxp_fudup*v1.max,-minp_fudup*v1.max) v2.mul(-1)
  }
  v1.updown(v2,tl,bb)
  if (pos_fudup) { bq.v[1].add(min) bq.v[3].add(min) }
  cq.append(bq)
  sz=bq.size(1)
  if (allover_fudup) { // do it upside down as well
    v1.mul(-1) // v2 will be upside-down
    if (pos_fudup) {min=v1.min v1.sub(min)}
    if (0) {  // can't see a rationale to recalc sampling points
      v2.indgen(2,2+npts-1,1)   // sampling at npts points
      if (logflag) v2.log() // log sampling
      v2.scale(-0.95*v1.max,-0.05*v1.max) v2.mul(-1)
    }
    v1.updown(v2,tl,bb)
    bq.v[8].add(sz) bq.v[4].mul(-1) // turn HEIGHT upside down
    cq.append(bq)
  } else if (over_fudup && sz==0) { // turn it over an try again
    print "fudup() checking upside-down"
    v1.mul(-1) // v2 will be upside-down
    v1.updown(v2,tl,bb)
  } 
  for case(&x,0,2,5) cq.v[x].mul(dt)
  nqsdel(bq,eq)
  dealloc(a)
  return cq
}

//** listsort(LIST[,START,REV]) sorts list of strings numerically
// optional start gives a regexp to start at
proc listsort () { local x,rev localobj nq,st,xo
  if (numarg()==0) { 
    print "listsort(LIST[,RXP,REV]) numerically, optional RXP starts after there" return}
  if (numarg()==3) if ($3) rev=-1 else rev=0
  nq=new NQS("STR","NUM") nq.strdec("STR")
  st=new String()
  for ltr(xo,$o1) {
    if (numarg()>=2) sfunc.tail(xo.s,$s2,st.s) else st.s=xo.s
    if (sscanf(st.s,"%g",&x)!=1) print "listsort ERR: num not found in ",st.s
    nq.append(xo.s,x)
  } 
  nq.sort("NUM",rev)
  $o1.remove_all
  for nq.qt(st.s,"STR") $o1.append(new String(st.s))
}

// stat(VEC) print stats for the vector
// stat(VEC1,VEC2) append stats of VEC1 on VEC2
// stat(VEC1,NQS) append stats of VEC1 on NQS (create if necessary)
proc stat () { local sz
  sz=$o1.size
  if (sz<=1) {printf("decnqs::stat() WARN: %s size %d\n",$o1,$o1.size) return}
  if (numarg()==1 && sz>1) {
    printf("Sz:%d\tmax=%g; min=%g; mean=%g; stdev=%g\n",$o1.size,$o1.max,$o1.min,$o1.mean,$o1.stdev)
  } else {
    if (!isassigned($o2)) { $o2=new NQS("SIZE","MAX","MIN","MEAN","STDEV")
    } else if (isobj($o2,"NQS")) { 
      if ($o2.m!=5) {$o2.resize(0) $o2.resize("SIZE","MAX","MIN","MEAN","STDEV")}
    } else if (isobj($o2,"Vector")) revec($o2)
    if (sz>2) {$o2.append($o1.size,$o1.max,$o1.min,$o1.mean,$o1.stdev) // .append for Vector or NQS
    } else   $o2.append($o1.size,$o1.max,$o1.min,$o1.min,0) // no sdev
  }
}

//* fil2nqs(FILE,NQS) reads lines of file and places all numbers in NQS
func fil2nqs () { local a,n localobj v1
  $o2.clear
  a=allocvecs(v1)
  tmpfile.ropen($s1)
  for (n=1;tmpfile.gets(tstr)!=-1;n+=1) {
    if (n%1e3==0) printf("%d ",n)
    parsenums(tstr,v1)
    if (v1.size!=$o2.m) {
      printf("Wrong size at line %d (%d)  ",n,v1.size)  vlk(v1)
      return
    }
    $o2.append(v1)
  }
  dealloc(a)
  return $o2.size(1)
}

//** plnqs(file,NQS) reads output of txt2num.pl
// format ascii 'rows cols' then binary contents
proc plnqs () { local a,rows,cols localobj v1,v2
  a=allocvecs(v1,v2)
  tmpfile.ropen($s1)
  tmpfile.gets(tstr)
  sscanf(tstr,"%d %d",&rows,&cols)
  printf("%s: %d rows x %d cols\n",$s1,rows,cols)
  v1.fread(tmpfile,rows*cols) // could now use .transpose
  v2.indgen(0,rows*cols,cols)
  $o2.resize(cols,rows)
  for ii=0,cols-1 {
    v2.add(ii)
    $o2.v[ii].index(v1,v2)
  }
  dealloc(a)
}
    
// DEST=maxem(SRC,MIN,WIDTH) -- keep looking for maxima till get down to min
obfunc maxem () { local a,min,wid,ii,ix,beg,end localobj v1,aq
  a=allocvecs(v1)
  aq=new NQS("max","loc") aq.clear(v1.size/2)
  v1.copy($o1)
  min=$2 wid=$3
  while(v1.max>min) {
   aq.append(v1.max,ix=v1.max_ind)
   beg=ix-wid if (beg<0) beg=0
   end=ix+wid if (end>=v1.size) end=v1.size-1
   for ii=beg,end v1.x[ii]=-1e9
  }
  dealloc(a)
  return aq
}

// nqo=percl(nq,"COLA", ..) generates NQS of percentile values (10..90) for these cols
// nqo=percl(nq,min,max,step,"COLA", ..) -- eg percl(nq,50,70,5,"COLA","COLB")
obfunc percl () {  local i,ii,a,p localobj v1,v2,v3,aq,xo
  a=allocvecs(v1,v2,v3)
  aq=new NQS(numarg())
  if (argtype(2)==0) {
    v3.indgen($2,$3,$4) 
    aq.resize(aq.size(1)-3)
    i=5 j=4 // start at arg i and aq col #j
  } else {
    v3.indgen(10,90,10)
    i=2 j=1
  }
  aq.setcol(0,"PERCL",v3)
  for (;i<=numarg();i+=1) {
    $o1.getcol($si,v1)
    v1.sort()
    v2.resize(0)
    for vtr(&ii,v3) {ii/=100 v2.append(v1.x[round(ii*v1.size)])}
    aq.setcol(i-j,$si,v2)
  }
  dealloc(a)
  return aq
}

// pqunq(NQS) returns columns of sorted nique values corresponding to the arg
// NB: does not produce a rectangular array
obfunc pqunq () { local a localobj v1,v2,aq
  aq=new NQS()
  a=allocvecs(v1,v2,1e5)
  aq.sethdrs($o1)
  aq.resize(-2) 
  for ii=0,aq.m-1 {
    $o1.getcol(ii,v1)
    v1.sort 
    v2.redundout(v1)
    aq.v[ii].copy(v2)
  }
  dealloc(a)
  return aq
}

//** aa=seqind(ind) -- find beginning and end of sequential indices with 
obfunc seqind () { local a,n,skip,ii,x,last localobj vi,oq
  vi=$o1
  if (numarg()>=2) skip=$2+1 else skip=1
  if (numarg()>=3) oq=$o3
  if (!isassigned(oq)) {oq=new NQS() if (numarg()>=3) $o3=oq}
  if (oq.m!=3) { oq.resize(0) oq.resize("beg","end","diff") }
  oq.clear()
  n=last=0
  for ii=1,vi.size(1)-1 {
    if (vi.x[ii]-vi.x[ii-1]>skip) {
      if (n>0) oq.append(vi.x[last],vi.x[ii-1],0)
      last=ii
      n=0
    } else n+=1
  }
  if (n>0) oq.append(vi.x[last],vi.x[ii-1],0)
  oq.pad()
  oq.calc("<diff>.copy(<end>.c.sub(<beg>))")
  return oq
}

//** list_transpose
proc list_transpose () { localobj aq,mat,xo,inlist,outlist
  aq=new NQS() inlist=$o1 outlist=$o2
  if (!isojt(outlist,inlist)) {outlist=new List() $o2=outlist}
  for ltr(xo,inlist) aq.resize("",xo)
  mat=aq.tomat(1) // transpose
  aq.frmat(mat)
  outlist.remove_all
  aq.listvecs(outlist)
  delnqs(aq)
}

//* Sam's additions -- moved from nqs_utils.hoc
//get row of Vectors
//$o1 = nqs
//$2 = row number
//$s3 - $snumarg() - name of cols to get values for
//returns list with associated Vectors
obfunc getobjrow(){ local i,rowid localobj nq,vt,ls
  nq=$o1 rowid=$2
  ls=new List()
  vt=new Vector()
  for(i=3;i<=numarg();i+=1){
    nq.get($si,rowid,vt)
    ls.append(vt)
  }
  return ls
}

//get column of objects as Vector using oform
//$o1 = nqs
//$s2 = col name
//$3=iff==1 return list of Vectors in column, else return vector of oform of each row in column
obfunc getobjcol(){ local idx,getl localobj nq,vt,vt2,ls
  nq=$o1
  if(numarg()>2) getl=$3 else getl=0
  if(getl){
    ls=new List()
    vt=new Vector()
    for idx=0,nq.size-1{
      nq.get($s2,idx,vt)
      ls.append(vt)
    }
    return ls
  } else {
    vt=new Vector(nq.size)
    vt.resize(0)
    vt2=new Vector()
    for idx=0,nq.size-1{
      nq.get($s2,idx,vt2)
      vt.append(oform(vt2))
    }
    return vt
  }
}

//get correlation between 2 columns of an NQS
//$o1=nqs
//$s2=column name
//$s3=column name
//$4=pearson correlation iff == 1 (default), otherwise spearman
func nqcor(){ local pc localobj nq1,v1,v2
  if(numarg()>3) pc=$4 else pc=1
  nq1=$o1
  v1=nq1.getcol($s2)  v2=nq1.getcol($s3)
  if (pc) return v1.pcorrel(v2) else return v1.scorrel(v2)
}

//func nqgrslice(){ local startidx,endidx localobj nq,vtmp
//  nq=$o1 startidx=$2 endidx=$3
//  vtmp=new Vector($3-$2+1)
//  gg(
//}

func MIN(){ if($1<$2)return $1 else return $2 }

//get correlation matrix/nqs of all columns to all columns
//$o1 = NQS
//$2 = num columns 0 - NQS.m
//$3 = start row/index
//$4 = end row/index
//$5 = index increment
//$6 = window size , iff <= 0, do full columns against each other
obfunc nqcolcor(){ local startidx,endidx,inct,wint,c1,c2,ncol localobj vhr,vhl,nqc,nqf
 if(numarg()<1){
   printf("nqcolcor usage: \n\t$o1 = NQS\
                           \n\t$2 = num columns 0 - NQS.m\
                           \n\t$3 = start row/index\
                           \n\t$4 = end row/index\
                           \n\t$5 = index increment\
                           \n\t$6 = window size , iff <= 0, do full columns against each other\n")
   return nil
 }
 nqf=$o1
 if(numarg()>1) ncol=$2 else ncol=nqf.m
 if(numarg()>2) startidx=$3 else startidx=0
 if(numarg()>3) endidx=$4 else endidx=nqf.size
 if(numarg()>4) inct=$5 else inct=50*2//50ms
 if(numarg()>5) wint=$6 else wint=100*2//50ms

 vhr=new Vector(wint) vhl=new Vector(wint)

 if(wint<=0){ //full column cross-correlation
   nqc=new NQS("ID0","ID1","cor") 
   for c1=0,ncol-1{
     for c2=c1+1,ncol-1{
       nqc.append(c1,c2,nqf.v[c1].pcorrel(nqf.v[c2]))
     }
   }
 } else { //cross correlation using slices of column
   nqc=new NQS("ID0","ID1","start","end","cor") 
   for c1=0,ncol-1{
     for c2=c1+1,ncol-1{
       for(startidx=0;startidx<endidx;startidx+=inct){
         vhl.copy(nqf.v[c1],startidx,MIN(startidx+wint,endidx-1))
         vhr.copy(nqf.v[c2],startidx,MIN(startidx+wint,endidx-1))
         nqc.append(c1,c2,startidx,startidx+wint,vhl.pcorrel(vhr))
       }
     }
   }
 }

 return nqc
}

 
//read wmf ascii file (just skips header and calls rdcol)
//$s1 = wmf file path
//$2 = # of columns
// obfunc rdwmf(){ local idx,jdx,hdrlines localobj nq,myf,myftmp,strf,str,strtmp,lcols
// myf=new File() myftmp=new File() strf=new StringFunctions() str=new String() lcols=new List()
// strtmp=new String() hdrlines=6
// myf.ropen($s1)
// if(!myf.is_open()){
//  printf("rdwmf ERRA: couldn't open wmf file %s for read\n",$s1)
//  return nil
// }
// for idx=0,hdrlines-1{
//  if(myf.gets(str.s)==-1){
//    printf("rdwmf ERRB: corrupt header\n")
//    return nil
//  } else if(idx==2){
//    jdx=strf.tail(str.s,"",strtmp.s)            
//  }
// }
// myf.close()
// return nq
// }

//draw regression line
//$o1 = nqs, $s2 = column 1, $s3 = column 2
// or
//$o1 = Vector 1 , $o2 = Vector 2
//returns vo
obfunc drawregline(){ local x0,y0,x1,y1,gvtmp,r localobj vo,nq,v1,v2,vx,vy,str
  vo=new Vector(5)
  if(numarg()==3){
    nq = $o1
    v1=new Vector()  v2=new Vector()
    nq.getcol($s2,v1)
    nq.getcol($s3,v2)
  } else {
    v1=$o1 v2=$o2
  }
  v1.vstats(v2,vo)
  x0 = v2.min
  y0 = x0*vo.x(0)+vo.x(1)
  x1 = v2.max
  y1 = x1*vo.x(0)+vo.x(1)
  vx=new Vector(2)
  vx.x(0)=x0
  vx.x(1)=x1
  vy=new Vector(2)
  vy.x(0)=y0
  vy.x(1)=y1
  gvtmp=gvmarkflag
  gvmarkflag=0
  gg(vy,vx)
  gvmarkflag=gvtmp
  str=new String()
  r=v1.pcorrel(v2)
  if(name_declared("rpval_stats")){
    sprint(str.s,"r = %.2f, p = %g, N = %d",r,rpval_stats(v1.size,r),v1.size)
    g.label(0,0,str.s)
  }
  return vo
}

// select from a vector handled as a matrix -- see matrix.mod
halfmat=0
obfunc mindsel () { local a,x,r,c localobj vm,vi,oq
  vm=$o1 
  a=allocvecs(vi)
  if (numarg()==4) vi.indvwhere(vm,$s2,$3,$4) else vi.indvwhere(vm,$s2,$3)
  oq=new NQS("row","col","val") 
  oq.clear(vi.size)
  for vtr(&x,vi) {
    r=int(x/COLS) c=x-r*COLS
    if (!halfmat || c>r) oq.append(r,c,vm.x[x])
  }
  dealloc(a)
  print oq.size(1)
  return oq
}

//* return row $2 of nqs $o1 
obfunc nqrow () { local row,col localobj vout,nq
  nq=$o1
  row=$2
  vout=new Vector(nq.m)
  for col=0,nq.m-1 vout.x(col)=nq.v[col].x(row)
  return vout
}

//* find row $o2 (vector) in $o1 (nqs) and return index
//  if not there return -1
func nqfindrow () { local idx,jdx,sz localobj nq,vf,vrow
  nq=$o1  vf=$o2
  sz=nq.size(-1)
  for idx=0,sz-1 {
    vrow = nqrow(nq,idx)
    if(vrow.eq(vf)) return idx
  }
  return -1
}

//* nquniq(NQS) -- return a new NQS with unique rows in $o1
obfunc nquniq () { local sz,idx,outrow,jdx localobj nqin,nqout,vrow
  nqin=$o1
  nqout=new NQS()
  for idx=0,nqin.m-1{
    nqout.resize(nqin.s[idx].s)
    nqout.v[nqout.m-1].resize(nqin.v[idx].size)
  }
  sz=nqin.size(-1)
  jdx=0
  outrow=0
  for idx=0,sz-1{
    vrow=nqrow(nqin,idx)
    if(nqfindrow(nqout,vrow)==-1){
      for jdx=0,nqin.m-1 nqout.v[jdx].x(outrow) = vrow.x(jdx)
      outrow += 1
    }
  }
  for idx=0,nqout.m-1 nqout.v[idx].resize(outrow)
  return nqout
}

Rowan MS, Neymotin SA, Lytton WW (2014) Electrostimulation to reduce synaptic scaling driven progression of Alzheimer's disease. Front Comput Neurosci 8:39[PubMed]

References and models cited by this paper

References and models that cite this paper

Binzegger T, Douglas RJ, Martin KA (2004) A quantitative map of the circuit of cat primary visual cortex. J Neurosci 24:8441-53 [PubMed]

Busche MA, Eichhoff G, Adelsberger H, Abramowski D, Wiederhold KH, Haass C, Staufenbiel M, Ko (2008) Clusters of hyperactive neurons near amyloid plaques in a mouse model of Alzheimer's disease. Science 321:1686-9

Carnevale NT, Hines ML (2006) The NEURON Book

Chandler B, Grossberg S (2012) Joining distributed pattern processing and homeostatic plasticity in recurrent on-center off-surround shunting networks: noise, saturation, short-term memory, synaptic scaling, and BDNF. Neural Netw 25:21-9

Crumiller M, Knight B, Yu Y, Kaplan E (2011) Estimating the amount of information conveyed by a population of neurons. Front Neurosci 5:90-31 [PubMed]

Demuro A, Parker I, Stutzmann GE (2010) Calcium signaling and amyloid toxicity in Alzheimer disease. J Biol Chem 285:12463-8

Frohlich F, Bazhenov M, Sejnowski TJ (2008) Pathological effect of homeostatic synaptic scaling on network dynamics in diseases of the cortex. J Neurosci 28:1709-20 [PubMed]

Gourevitch B, Eggermont JJ (2007) Evaluating information transfer between auditory cortical neurons. J Neurophysiol 97:2533-43 [PubMed]

Hansen N (2012) Action mechanisms of transcranial direct current stimulation in Alzheimer's disease and memory loss. Front Psychiatry 3:48-8

Kerr CC, Neymotin SA, Chadderdon GL, Fietkiewicz CT, Francis JT, Lytton WW (2012) Electrostimulation as a prosthesis for repair of information flow in a computer model of neocortex IEEE Transactions on Neural Systems & Rehabilitation Engineering 20(2):153-60 [Journal] [PubMed]

   Prosthetic electrostimulation for information flow repair in a neocortical simulation (Kerr 2012) [Model]

Lefort S, Tomm C, Floyd Sarria JC, Petersen CC (2009) The excitatory neuronal network of the C2 barrel column in mouse primary somatosensory cortex. Neuron 61:301-16 [PubMed]

Liebetanz D, Koch R, Mayenfels S, Konig F, Paulus W, Nitsche MA (2009) Safety limits of cathodal transcranial direct current stimulation in rats. Clin Neurophysiol 120:1161-7

Lytton WW, Neymotin SA, Kerr CC (2014) Multiscale modeling for clinical translation in neuropsychiatric disease J Comput Surg 1:7

Lytton WW, Omurtag A, Neymotin SA, Hines ML (2008) Just in time connectivity for large spiking networks Neural Comput 20(11):2745-56 [Journal] [PubMed]

   JitCon: Just in time connectivity for large spiking networks (Lytton et al. 2008) [Model]

Lytton WW, Stewart M (2005) A rule-based firing model for neural networks Int J Bioelectromagn 7:47-50

Lytton WW, Stewart M (2006) Rule-based firing for network simulations. Neurocomputing 69:1160-1164

Modolo J, Beuter A (2009) Linking brain dynamics, neural mechanisms, and deep brain stimulation in Parkinson's disease: an integrated perspective. Med Eng Phys 31:615-23

Neymotin S, Kerr C, Francis J, Lytton W (2011) Training oscillatory dynamics with spike-timing-dependent plasticity in a computer model of neocortex Signal Processing in Medicine and Biology Symposium (SPMB), IEEE :1-6

Neymotin SA, Lee H, Park E, Fenton AA, Lytton WW (2011) Emergence of physiological oscillation frequencies in a computer model of neocortex. Front Comput Neurosci 5:19-75 [Journal] [PubMed]

   Emergence of physiological oscillation frequencies in neocortex simulations (Neymotin et al. 2011) [Model]

Palop JJ, Mucke L (2010) Amyloid-beta-induced neuronal dysfunction in Alzheimer's disease: from synapses toward neural networks. Nat Neurosci 13:812-8 [PubMed]

Qiu S, Anderson CT, Levitt P, Shepherd GM (2011) Circuit-specific intracortical hyperconnectivity in mice with deletion of the autism-associated Met receptor tyrosine kinase. J Neurosci 31:5855-64

Rabey JM, Dobronevsky E, Aichenbaum S, Gonen O, Marton RG, Khaigrekht M (2013) Repetitive transcranial magnetic stimulation combined with cognitive training is a safe and effective modality for the treatment of Alzheimer's disease: a randomized, double-blind study. J Neural Transm 120:813-9

Rowan MS,Neymotin SA (2013) Synaptic Scaling Balances Learning in a Spiking Model of Neocortex Adaptive and Natural Computing Algorithms, Tomassini M, Antonioni A, Daolio F, Buesser P, ed. pp.20 [Journal]

   Synaptic scaling balances learning in a spiking model of neocortex (Rowan & Neymotin 2013) [Model]

Rutherford LC, Nelson SB, Turrigiano GG (1998) BDNF has opposite effects on the quantal amplitude of pyramidal neuron and interneuron excitatory synapses. Neuron 21:521-30 [PubMed]

Savioz A, Leuba G, Vallet PG, Walzer C (2009) Contribution of neural networks to Alzheimer disease's progression. Brain Res Bull 80:309-14

Shepherd GM (2013) Corticostriatal connectivity and its role in disease. Nat Rev Neurosci 14:278-91

Small DH (2008) Network dysfunction in Alzheimer's disease: does synaptic scaling drive disease progression? Trends Mol Med 14:103-8 [PubMed]

Smith GS, Laxton AW, Tang-Wai DF, McAndrews MP, Diaconescu AO, Workman CI, Lozano AM (2012) Increased cerebral metabolism after 1 year of deep brain stimulation in Alzheimer disease. Arch Neurol 69:1141-8 [PubMed]

Trasande CA, Ramirez JM (2007) Activity deprivation leads to seizures in hippocampal slice cultures: is epilepsy the consequence of homeostatic plasticity? J Clin Neurophysiol 24:154-64 [PubMed]

Turrigiano G (2011) Too many cooks? Intrinsic and synaptic homeostatic mechanisms in cortical circuit refinement. Annu Rev Neurosci 34:89-103 [PubMed]

Turrigiano GG (2008) The self-tuning neuron: synaptic scaling of excitatory synapses. Cell 135:422-35 [PubMed]

Turrigiano GG, Leslie KR, Desai NS, Rutherford LC, Nelson SB (1998) Activity-dependent scaling of quantal amplitude in neocortical neurons. Nature 391:892-6 [PubMed]

Utz KS, Dimova V, Oppenlander K, Kerkhoff G (2010) Electrified minds: transcranial direct current stimulation (tDCS) and galvanic vestibular stimulation (GVS) as methods of non-invasive brain stimulation in neuropsychology--a review of current data and future implications. Neuropsychologia 48:2789-810

van Rossum MC, Bi GQ, Turrigiano GG (2000) Stable Hebbian learning from spike timing-dependent plasticity. J Neurosci 20:8812-21 [PubMed]

Weiler N, Wood L, Yu J, Solla SA, Shepherd GM (2008) Top-down laminar organization of the excitatory network in motor cortex. Nat Neurosci 11:360-6 [Journal] [PubMed]

   Laminar connectivity matrix simulation (Weiler et al 2008) [Model]

Yu Y, Crumiller M, Knight B, Kaplan E (2010) Estimating the amount of information carried by a neuronal population. Front Comput Neurosci 4:10-810

Dura-Bernal S, Li K, Neymotin SA, Francis JT, Principe JC, Lytton WW (2016) Restoring behavior via inverse neurocontroller in a lesioned cortical spiking model driving a virtual arm. Front. Neurosci. Neuroprosthetics 10:28 [Journal]

   Cortical model with reinforcement learning drives realistic virtual arm (Dura-Bernal et al 2015) [Model]

Neymotin SA, McDougal RA, Sherif MA, Fall CP, Hines ML, Lytton WW (2015) Neuronal calcium wave propagation varies with changes in endoplasmic reticulum parameters: a computer model Neural Computation 27(4):898-924 [Journal] [PubMed]

   Neuronal dendrite calcium wave model (Neymotin et al, 2015) [Model]

(38 refs)